A very important image processing tool used in machine vision automatically identifies given features within the image. Up to recently, this has almost always been accomplished using a monochrome system in conjunction with some form of search algorithm. However, there are situations where there is insufficient grey level difference between the intended target and the background for the target to be reliably identified in this manner. In many cases, switching from a monochrome imaging system to a color system can be of great assistance, relying on using color discrimination to separate the target from the background.
U.S. Pat. No. 5,751,450 discloses a tool which has been designed to ease the construction of such color based discriminators using what is, in effect, a programmable color filter. However, there is an important class of images in which the color difference is insufficient for the straight forward color filter to work effectively.
Conventionally, color images are captured and manipulated in a color space defined by the red, green, and blue axes, as illustrated in FIG. 2. This is primarily because both the human eye, and virtually all color cameras come equipped with sensors that are sensitive to these colors, and so it becomes second nature to operate in such a color space, or one closely related to it. However, in many images this choice of axes could be far from optimum, especially if there is any information common to the red, green, and blue axes.
An example of the worse case of such a distribution is shown in FIG. 2. Here, every pixel in a sample image is plotted in its correct position in the RGB color space. The result, in this case, has a very strong correlation to a straight line that is at an angle of 45.degree. to all of the axes. With this type of distribution, the image information is effectively shared equally over the three color planes. Although this represents the most unfavorable type of distribution, it is not particularly uncommon - occurring anywhere that the predominant color is grey, for example imaging aluminum or stainless steel structures.
U.S. Pat. No. 4,653,014 to Mitcami et al. discloses a process for preparing discrimination criteria for identifying colors in a color identifying system. A computer-based system attempts to automatically determine the best way in which to be able to distinguish between single color samples. The system must be shown the color which it is trying to identify. It will then automatically derive the optimum parameters with which to be able to differentiate samples of these colors. It describes a spot measuring instrument rather than an area measuring instrument. Its output is essentially digital, inasmuch as the output result is that a sample is identified as being identical to one of the original samples. There is no way to quantitatively measuring how close, or how far the sample is from the original reference.
U.S. Pat. No. 5,218,555 to Komai et al. discloses a method for judging a color difference using rules and fuzzy inference and apparatus therefor. The color matching system is totally based on "fuzzy logic" and "fuzzy inference" components within its algorithm.
U.S. Pat. No. 5,410,637 to Kern et al. discloses a color tolerancing system employing fuzzy logic. This technique appears to be designed for matching a single color sample. As with the Komai et al. patent, the algorithm is totally reliant on "fuzzy logic".
U.S. Pat. No. 5,085,325 to Jones et al. discloses a color sorting system and method. The system uses a polar coordinate color specification. An external look up table is used to perform an acceptance/rejection flag. Digitized video goes straight into the hardware-implemented look up table for real-time speed.
U.S. Pat. No. 5,221,959 to Ohyama et al. discloses a color discrimination data input apparatus. The system modifies the spectral content of the illumination on an object in order to obtain the optimum separation of several classes of object based on differences in spectral reflectance of the objects being classified.
U.S. Pat. No. 4,414,635 to Gast et al. discloses a system for segmenting an image based on certain key hues. The system is trained on a series of keystone colors, each of which is given a label. These colors only cover a small segment of the color space, thereby leaving many colors unlabeled. In order to ensure that every color has a label, Gast uses color distance to determine which key color any unlabeled hue is closest to and allows that color to adopt the label of its neighboring key color. From this process, Gast generates a look up table whose address defines the color of the current pixel and the output generates the label. By scanning another image pixel by pixel, each part of the new image can be allocated a label. Gast extends the system to produce a complementary facility whereby labels can be converted back to the key color. Thus, by using a pre-trained segmenter to scan an image followed by a synchronized complementary system, Gast can generate a duplicate image which only contains the key hues.
U.S. Pat. No. 5,552,805 to Alpher is also related to the present invention.